{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# eXtreme Gradient Boosting (Reduced Featureset 1)\n",
    "## Hyperparameter tuning is performed on reduced feature set 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Get the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Stored variables and their in-db values:\n",
      "X_16_val                  -> array([[ 0.10924883,  1.83030605, -0.14807631, ...\n",
      "X_32_val                  -> array([[ 0.66944195,  0.46536115,  0.79919788, ...\n",
      "X_32test_new              -> defaultdict(<class 'list'>, {0: array([[ 0.6694419\n",
      "X_32test_std              -> defaultdict(<class 'list'>, {0: array([[ 0.6694419\n",
      "X_32train_new             -> array([[-0.74031227,  0.0126481 , -0.30967801, ...\n",
      "X_32train_std             -> array([[-0.74031227,  0.0126481 , -0.30967801, ...\n",
      "X_test                    -> defaultdict(<class 'list'>, {0: array([[[ -6.40490\n",
      "X_test_new                -> defaultdict(<class 'list'>, {0: array([[ 0.1092488\n",
      "X_test_std                -> defaultdict(<class 'list'>, {0: array([[ 0.1092488\n",
      "X_train                   -> array([[[ 0.00119031,  0.00873315,  0.00641749, ..\n",
      "X_train_new               -> array([[-0.74031227,  0.23616372, -0.18182195, ...\n",
      "X_train_std               -> array([[-0.74031227,  0.0126481 , -0.30967801, ...\n",
      "snrs                      -> [-20, -18, -16, -14, -12, -10, -8, -6, -4, -2, 0, \n",
      "y_16_val                  -> array([6, 6, 5, ..., 0, 4, 1])\n",
      "y_32_test                 -> defaultdict(<class 'list'>, {0: array([2, 2, 4, ..\n",
      "y_32_train                -> array([0, 3, 4, ..., 0, 3, 1])\n",
      "y_32_val                  -> array([2, 2, 4, ..., 0, 7, 3])\n",
      "y_test                    -> defaultdict(<class 'list'>, {0: array([6, 6, 5, ..\n",
      "y_train                   -> array([0, 3, 4, ..., 0, 3, 1])\n"
     ]
    }
   ],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from collections import defaultdict\n",
    "import xgboost as xgb\n",
    "from xgboost.sklearn import XGBClassifier\n",
    "from sklearn.cross_validation import train_test_split\n",
    "from sklearn.datasets import dump_svmlight_file\n",
    "from sklearn.externals import joblib\n",
    "from sklearn.metrics import precision_score\n",
    "from sklearn.grid_search import GridSearchCV\n",
    "from sklearn import cross_validation, metrics\n",
    "from time import time\n",
    "import pandas as pd\n",
    "import collections\n",
    "\n",
    "%store -r\n",
    "%store"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data:  (80000, 11) and labels:  (80000,)\n",
      " \n",
      "Test data:\n",
      "Total 20 (4000, 11) arrays for SNR values:\n",
      "[-20, -18, -16, -14, -12, -10, -8, -6, -4, -2, 0, 2, 4, 6, 8, 10, 12, 14, 16, 18]\n"
     ]
    }
   ],
   "source": [
    "print(\"Training data: \", X_train_new.shape, \"and labels: \", y_train.shape)\n",
    "print(\" \")\n",
    "print(\"Test data:\")\n",
    "print(\"Total\", len(X_test_new), X_test_new[18].shape, \"arrays for SNR values:\")\n",
    "print(sorted(X_test_new.keys()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Train and test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test set (80000, 11) and labels (80000,)\n"
     ]
    }
   ],
   "source": [
    "#Unfold all test data into one array\n",
    "\n",
    "from sklearn.utils import shuffle\n",
    "\n",
    "X_test1 = []\n",
    "y_test1 = []\n",
    "for snr in snrs:\n",
    "    X_test1.append(X_test_new[snr])\n",
    "    y_test1.append(y_test[snr])\n",
    "\n",
    "X_test1 = np.vstack(X_test1)\n",
    "y_test1 = np.hstack(y_test1)\n",
    "X_test1, y_test1 = shuffle(X_test1, y_test1)\n",
    "print(\"Test set {} and labels {}\".format(X_test1.shape, y_test1.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tune max_depth and min_child_weight"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Grid search took 18.17933406829834 minutes\n"
     ]
    }
   ],
   "source": [
    "param_grid1 = {'max_depth':list(range(3,10,2)), 'min_child_weight': list(range(1,6,2))}\n",
    "\n",
    "start = time()\n",
    "\n",
    "grid_search1 = GridSearchCV(estimator = XGBClassifier(max_depth=3, eta=0.3, objective='multi:softprob', \n",
    "                                                      num_class=8, early_stopping_rounds=20), \n",
    "                       param_grid = param_grid1, scoring='accuracy',n_jobs=4)\n",
    "\n",
    "grid_search1.fit(X_train_new, y_train)\n",
    "print(\"Grid search took {} minutes\".format((time()-start)/60))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([mean: 0.48840, std: 0.00100, params: {'max_depth': 3, 'min_child_weight': 1},\n",
       "  mean: 0.48857, std: 0.00068, params: {'max_depth': 3, 'min_child_weight': 3},\n",
       "  mean: 0.48857, std: 0.00117, params: {'max_depth': 3, 'min_child_weight': 5},\n",
       "  mean: 0.49794, std: 0.00169, params: {'max_depth': 5, 'min_child_weight': 1},\n",
       "  mean: 0.49734, std: 0.00118, params: {'max_depth': 5, 'min_child_weight': 3},\n",
       "  mean: 0.49788, std: 0.00109, params: {'max_depth': 5, 'min_child_weight': 5},\n",
       "  mean: 0.50115, std: 0.00082, params: {'max_depth': 7, 'min_child_weight': 1},\n",
       "  mean: 0.50020, std: 0.00106, params: {'max_depth': 7, 'min_child_weight': 3},\n",
       "  mean: 0.50191, std: 0.00165, params: {'max_depth': 7, 'min_child_weight': 5},\n",
       "  mean: 0.50099, std: 0.00060, params: {'max_depth': 9, 'min_child_weight': 1},\n",
       "  mean: 0.50176, std: 0.00050, params: {'max_depth': 9, 'min_child_weight': 3},\n",
       "  mean: 0.50151, std: 0.00058, params: {'max_depth': 9, 'min_child_weight': 5}],\n",
       " {'max_depth': 7, 'min_child_weight': 5},\n",
       " 0.5019125)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search1.grid_scores_, grid_search1.best_params_, grid_search1.best_score_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Narrow down parameter values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Grid search took 16.33037612438202 minutes\n"
     ]
    }
   ],
   "source": [
    "param_grid2 = {'max_depth':[6,7,8], 'min_child_weight': [4,5,6]}\n",
    "\n",
    "start = time()\n",
    "\n",
    "grid_search2 = GridSearchCV(estimator = XGBClassifier(eta=0.3, objective='multi:softprob', \n",
    "                                                      num_class=8, early_stopping_rounds=20), \n",
    "                       param_grid = param_grid2, scoring='accuracy',n_jobs=4)\n",
    "\n",
    "grid_search2.fit(X_train_new, y_train)\n",
    "print(\"Grid search took {} minutes\".format((time()-start)/60))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([mean: 0.50061, std: 0.00062, params: {'max_depth': 6, 'min_child_weight': 4},\n",
       "  mean: 0.50123, std: 0.00135, params: {'max_depth': 6, 'min_child_weight': 5},\n",
       "  mean: 0.50072, std: 0.00038, params: {'max_depth': 6, 'min_child_weight': 6},\n",
       "  mean: 0.50245, std: 0.00179, params: {'max_depth': 7, 'min_child_weight': 4},\n",
       "  mean: 0.50191, std: 0.00165, params: {'max_depth': 7, 'min_child_weight': 5},\n",
       "  mean: 0.50174, std: 0.00092, params: {'max_depth': 7, 'min_child_weight': 6},\n",
       "  mean: 0.50305, std: 0.00042, params: {'max_depth': 8, 'min_child_weight': 4},\n",
       "  mean: 0.50097, std: 0.00084, params: {'max_depth': 8, 'min_child_weight': 5},\n",
       "  mean: 0.50165, std: 0.00122, params: {'max_depth': 8, 'min_child_weight': 6}],\n",
       " {'max_depth': 8, 'min_child_weight': 4},\n",
       " 0.50305)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search2.grid_scores_, grid_search2.best_params_, grid_search2.best_score_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Narrow down more"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Grid search took 17.34668723344803 minutes\n"
     ]
    }
   ],
   "source": [
    "param_grid2_1 = {'max_depth':[7,8,9], 'min_child_weight': [4,5,6]}\n",
    "\n",
    "start = time()\n",
    "\n",
    "grid_search2_1 = GridSearchCV(estimator = XGBClassifier(eta=0.3, objective='multi:softprob', \n",
    "                                                      num_class=8, early_stopping_rounds=20), \n",
    "                       param_grid = param_grid2_1, scoring='accuracy',n_jobs=4)\n",
    "\n",
    "grid_search2_1.fit(X_train_new, y_train)\n",
    "print(\"Grid search took {} minutes\".format((time()-start)/60))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([mean: 0.50245, std: 0.00179, params: {'max_depth': 7, 'min_child_weight': 4},\n",
       "  mean: 0.50191, std: 0.00165, params: {'max_depth': 7, 'min_child_weight': 5},\n",
       "  mean: 0.50174, std: 0.00092, params: {'max_depth': 7, 'min_child_weight': 6},\n",
       "  mean: 0.50305, std: 0.00042, params: {'max_depth': 8, 'min_child_weight': 4},\n",
       "  mean: 0.50097, std: 0.00084, params: {'max_depth': 8, 'min_child_weight': 5},\n",
       "  mean: 0.50165, std: 0.00122, params: {'max_depth': 8, 'min_child_weight': 6},\n",
       "  mean: 0.50139, std: 0.00119, params: {'max_depth': 9, 'min_child_weight': 4},\n",
       "  mean: 0.50151, std: 0.00058, params: {'max_depth': 9, 'min_child_weight': 5},\n",
       "  mean: 0.50105, std: 0.00059, params: {'max_depth': 9, 'min_child_weight': 6}],\n",
       " {'max_depth': 8, 'min_child_weight': 4},\n",
       " 0.50305)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search2_1.grid_scores_, grid_search2_1.best_params_, grid_search2_1.best_score_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tune gamma, while setting max_depth and min_child_weight to optimum values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Grid search took 10.950891117254892 minutes\n"
     ]
    }
   ],
   "source": [
    "param_grid3 = {'gamma':[i/10.0 for i in range(0,5)]}\n",
    "\n",
    "start = time()\n",
    "\n",
    "grid_search3 = GridSearchCV(estimator = XGBClassifier(max_depth= 8, min_child_weight= 4,\n",
    "                                                      eta=0.3, objective='multi:softprob', \n",
    "                                                      num_class=8, early_stopping_rounds=20), \n",
    "                       param_grid = param_grid3, scoring='accuracy',n_jobs=4)\n",
    "\n",
    "grid_search3.fit(X_train_new, y_train)\n",
    "print(\"Grid search took {} minutes\".format((time()-start)/60))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([mean: 0.50305, std: 0.00042, params: {'gamma': 0.0},\n",
       "  mean: 0.50114, std: 0.00118, params: {'gamma': 0.1},\n",
       "  mean: 0.50099, std: 0.00057, params: {'gamma': 0.2},\n",
       "  mean: 0.50249, std: 0.00002, params: {'gamma': 0.3},\n",
       "  mean: 0.50060, std: 0.00157, params: {'gamma': 0.4}],\n",
       " {'gamma': 0.0},\n",
       " 0.50305)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search3.grid_scores_, grid_search3.best_params_, grid_search3.best_score_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tune subsample and colsample_bytree; set max_depth, min_child_weight and gamma to optimum values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Grid search took 20.987338531017304 minutes\n"
     ]
    }
   ],
   "source": [
    "param_grid4 = {'subsample':[i/10.0 for i in range(6,10)], 'colsample_bytree':[i/10.0 for i in range(6,10)]}\n",
    "\n",
    "start = time()\n",
    "\n",
    "grid_search4 = GridSearchCV(estimator = XGBClassifier(max_depth= 8, min_child_weight= 4, gamma = 0.0,\n",
    "                                                      eta=0.3, objective='multi:softprob', \n",
    "                                                      num_class=8, early_stopping_rounds=20), \n",
    "                       param_grid = param_grid4, scoring='accuracy',n_jobs=4)\n",
    "\n",
    "grid_search4.fit(X_train_new, y_train)\n",
    "print(\"Grid search took {} minutes\".format((time()-start)/60))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([mean: 0.49939, std: 0.00132, params: {'colsample_bytree': 0.6, 'subsample': 0.6},\n",
       "  mean: 0.49976, std: 0.00182, params: {'colsample_bytree': 0.6, 'subsample': 0.7},\n",
       "  mean: 0.50072, std: 0.00094, params: {'subsample': 0.8, 'colsample_bytree': 0.6},\n",
       "  mean: 0.50185, std: 0.00099, params: {'subsample': 0.9, 'colsample_bytree': 0.6},\n",
       "  mean: 0.50104, std: 0.00141, params: {'colsample_bytree': 0.7, 'subsample': 0.6},\n",
       "  mean: 0.50235, std: 0.00136, params: {'colsample_bytree': 0.7, 'subsample': 0.7},\n",
       "  mean: 0.50225, std: 0.00050, params: {'subsample': 0.8, 'colsample_bytree': 0.7},\n",
       "  mean: 0.50149, std: 0.00105, params: {'subsample': 0.9, 'colsample_bytree': 0.7},\n",
       "  mean: 0.50010, std: 0.00265, params: {'colsample_bytree': 0.8, 'subsample': 0.6},\n",
       "  mean: 0.50087, std: 0.00039, params: {'colsample_bytree': 0.8, 'subsample': 0.7},\n",
       "  mean: 0.50101, std: 0.00096, params: {'subsample': 0.8, 'colsample_bytree': 0.8},\n",
       "  mean: 0.50252, std: 0.00146, params: {'subsample': 0.9, 'colsample_bytree': 0.8},\n",
       "  mean: 0.50280, std: 0.00144, params: {'colsample_bytree': 0.9, 'subsample': 0.6},\n",
       "  mean: 0.50109, std: 0.00186, params: {'colsample_bytree': 0.9, 'subsample': 0.7},\n",
       "  mean: 0.50226, std: 0.00064, params: {'subsample': 0.8, 'colsample_bytree': 0.9},\n",
       "  mean: 0.50400, std: 0.00057, params: {'subsample': 0.9, 'colsample_bytree': 0.9}],\n",
       " {'colsample_bytree': 0.9, 'subsample': 0.9},\n",
       " 0.504)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search4.grid_scores_, grid_search4.best_params_, grid_search4.best_score_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Narrow down search space for subsample and colsample_bytree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Grid search took 35.129989846547446 minutes\n"
     ]
    }
   ],
   "source": [
    "param_grid5 = {'subsample': [i/100.0 for i in range(75,100,5)],\n",
    "               'colsample_bytree':  [i/100.0 for i in range(75,100,5)]}\n",
    "\n",
    "start = time()\n",
    "\n",
    "grid_search5 = GridSearchCV(estimator = XGBClassifier(max_depth= 8, min_child_weight= 4, gamma = 0.0,\n",
    "                                                      eta=0.3, objective='multi:softprob', \n",
    "                                                      num_class=8, early_stopping_rounds=20), \n",
    "                       param_grid = param_grid5, scoring='accuracy',n_jobs=4)\n",
    "\n",
    "grid_search5.fit(X_train_new, y_train)\n",
    "print(\"Grid search took {} minutes\".format((time()-start)/60))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([mean: 0.50114, std: 0.00044, params: {'subsample': 0.75, 'colsample_bytree': 0.75},\n",
       "  mean: 0.50101, std: 0.00096, params: {'colsample_bytree': 0.75, 'subsample': 0.8},\n",
       "  mean: 0.50161, std: 0.00097, params: {'colsample_bytree': 0.75, 'subsample': 0.85},\n",
       "  mean: 0.50252, std: 0.00146, params: {'colsample_bytree': 0.75, 'subsample': 0.9},\n",
       "  mean: 0.50206, std: 0.00027, params: {'subsample': 0.95, 'colsample_bytree': 0.75},\n",
       "  mean: 0.50114, std: 0.00044, params: {'colsample_bytree': 0.8, 'subsample': 0.75},\n",
       "  mean: 0.50101, std: 0.00096, params: {'colsample_bytree': 0.8, 'subsample': 0.8},\n",
       "  mean: 0.50161, std: 0.00097, params: {'subsample': 0.85, 'colsample_bytree': 0.8},\n",
       "  mean: 0.50252, std: 0.00146, params: {'colsample_bytree': 0.8, 'subsample': 0.9},\n",
       "  mean: 0.50206, std: 0.00027, params: {'colsample_bytree': 0.8, 'subsample': 0.95},\n",
       "  mean: 0.50248, std: 0.00106, params: {'colsample_bytree': 0.85, 'subsample': 0.75},\n",
       "  mean: 0.50226, std: 0.00064, params: {'subsample': 0.8, 'colsample_bytree': 0.85},\n",
       "  mean: 0.50276, std: 0.00040, params: {'colsample_bytree': 0.85, 'subsample': 0.85},\n",
       "  mean: 0.50400, std: 0.00057, params: {'colsample_bytree': 0.85, 'subsample': 0.9},\n",
       "  mean: 0.50231, std: 0.00097, params: {'colsample_bytree': 0.85, 'subsample': 0.95},\n",
       "  mean: 0.50248, std: 0.00106, params: {'subsample': 0.75, 'colsample_bytree': 0.9},\n",
       "  mean: 0.50226, std: 0.00064, params: {'colsample_bytree': 0.9, 'subsample': 0.8},\n",
       "  mean: 0.50276, std: 0.00040, params: {'colsample_bytree': 0.9, 'subsample': 0.85},\n",
       "  mean: 0.50400, std: 0.00057, params: {'colsample_bytree': 0.9, 'subsample': 0.9},\n",
       "  mean: 0.50231, std: 0.00097, params: {'subsample': 0.95, 'colsample_bytree': 0.9},\n",
       "  mean: 0.50179, std: 0.00145, params: {'colsample_bytree': 0.95, 'subsample': 0.75},\n",
       "  mean: 0.50254, std: 0.00101, params: {'colsample_bytree': 0.95, 'subsample': 0.8},\n",
       "  mean: 0.50211, std: 0.00079, params: {'colsample_bytree': 0.95, 'subsample': 0.85},\n",
       "  mean: 0.50323, std: 0.00240, params: {'subsample': 0.9, 'colsample_bytree': 0.95},\n",
       "  mean: 0.50236, std: 0.00138, params: {'colsample_bytree': 0.95, 'subsample': 0.95}],\n",
       " {'colsample_bytree': 0.85, 'subsample': 0.9},\n",
       " 0.504)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search5.grid_scores_, grid_search5.best_params_, grid_search5.best_score_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fit the model to data and evaluate learning curves- inspect overfitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\tvalidation_0-merror:0.492825\tvalidation_0-mlogloss:1.93984\tvalidation_1-merror:0.510938\tvalidation_1-mlogloss:1.94295\n",
      "[1]\tvalidation_0-merror:0.486225\tvalidation_0-mlogloss:1.84224\tvalidation_1-merror:0.506325\tvalidation_1-mlogloss:1.8483\n",
      "[2]\tvalidation_0-merror:0.48045\tvalidation_0-mlogloss:1.76229\tvalidation_1-merror:0.504938\tvalidation_1-mlogloss:1.77096\n",
      "[3]\tvalidation_0-merror:0.477937\tvalidation_0-mlogloss:1.69532\tvalidation_1-merror:0.503213\tvalidation_1-mlogloss:1.70654\n",
      "[4]\tvalidation_0-merror:0.477\tvalidation_0-mlogloss:1.63946\tvalidation_1-merror:0.5025\tvalidation_1-mlogloss:1.65304\n",
      "[5]\tvalidation_0-merror:0.475188\tvalidation_0-mlogloss:1.59176\tvalidation_1-merror:0.502\tvalidation_1-mlogloss:1.60751\n",
      "[6]\tvalidation_0-merror:0.472988\tvalidation_0-mlogloss:1.54903\tvalidation_1-merror:0.50135\tvalidation_1-mlogloss:1.56713\n",
      "[7]\tvalidation_0-merror:0.47055\tvalidation_0-mlogloss:1.51181\tvalidation_1-merror:0.500713\tvalidation_1-mlogloss:1.53209\n",
      "[8]\tvalidation_0-merror:0.469275\tvalidation_0-mlogloss:1.47797\tvalidation_1-merror:0.499975\tvalidation_1-mlogloss:1.50045\n",
      "[9]\tvalidation_0-merror:0.46815\tvalidation_0-mlogloss:1.4481\tvalidation_1-merror:0.49995\tvalidation_1-mlogloss:1.47264\n",
      "[10]\tvalidation_0-merror:0.466188\tvalidation_0-mlogloss:1.42107\tvalidation_1-merror:0.498488\tvalidation_1-mlogloss:1.44758\n",
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      "[49]\tvalidation_0-merror:0.426725\tvalidation_0-mlogloss:1.12475\tvalidation_1-merror:0.491125\tvalidation_1-mlogloss:1.20229\n",
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      "[63]\tvalidation_0-merror:0.4123\tvalidation_0-mlogloss:1.09896\tvalidation_1-merror:0.490387\tvalidation_1-mlogloss:1.19201\n",
      "[64]\tvalidation_0-merror:0.410738\tvalidation_0-mlogloss:1.09738\tvalidation_1-merror:0.490375\tvalidation_1-mlogloss:1.19153\n",
      "[65]\tvalidation_0-merror:0.410287\tvalidation_0-mlogloss:1.09565\tvalidation_1-merror:0.490425\tvalidation_1-mlogloss:1.19104\n",
      "[66]\tvalidation_0-merror:0.408662\tvalidation_0-mlogloss:1.09397\tvalidation_1-merror:0.4908\tvalidation_1-mlogloss:1.19073\n",
      "[67]\tvalidation_0-merror:0.407488\tvalidation_0-mlogloss:1.09253\tvalidation_1-merror:0.4906\tvalidation_1-mlogloss:1.19039\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[68]\tvalidation_0-merror:0.4063\tvalidation_0-mlogloss:1.09125\tvalidation_1-merror:0.490387\tvalidation_1-mlogloss:1.19006\n",
      "[69]\tvalidation_0-merror:0.405475\tvalidation_0-mlogloss:1.09013\tvalidation_1-merror:0.490725\tvalidation_1-mlogloss:1.18982\n",
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      "[92]\tvalidation_0-merror:0.3774\tvalidation_0-mlogloss:1.06049\tvalidation_1-merror:0.49065\tvalidation_1-mlogloss:1.18575\n",
      "[93]\tvalidation_0-merror:0.376312\tvalidation_0-mlogloss:1.05924\tvalidation_1-merror:0.490288\tvalidation_1-mlogloss:1.18564\n",
      "[94]\tvalidation_0-merror:0.375125\tvalidation_0-mlogloss:1.05802\tvalidation_1-merror:0.49015\tvalidation_1-mlogloss:1.18558\n",
      "[95]\tvalidation_0-merror:0.373313\tvalidation_0-mlogloss:1.05645\tvalidation_1-merror:0.490263\tvalidation_1-mlogloss:1.18535\n",
      "[96]\tvalidation_0-merror:0.371975\tvalidation_0-mlogloss:1.05555\tvalidation_1-merror:0.490225\tvalidation_1-mlogloss:1.18528\n",
      "[97]\tvalidation_0-merror:0.369413\tvalidation_0-mlogloss:1.05364\tvalidation_1-merror:0.490025\tvalidation_1-mlogloss:1.18513\n",
      "[98]\tvalidation_0-merror:0.3686\tvalidation_0-mlogloss:1.0528\tvalidation_1-merror:0.490225\tvalidation_1-mlogloss:1.18515\n",
      "[99]\tvalidation_0-merror:0.367388\tvalidation_0-mlogloss:1.05155\tvalidation_1-merror:0.489975\tvalidation_1-mlogloss:1.18513\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "       colsample_bytree=0.85, early_stopping_rounds=20, eta=0.3, gamma=0.0,\n",
       "       learning_rate=0.1, max_delta_step=0, max_depth=8,\n",
       "       min_child_weight=4, missing=None, n_estimators=100, n_jobs=1,\n",
       "       nthread=None, num_class=8, objective='multi:softprob',\n",
       "       random_state=0, reg_alpha=0, reg_lambda=1, scale_pos_weight=1,\n",
       "       seed=None, silent=True, subsample=0.9)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Model with optimum values of hyperprameters \n",
    "model = XGBClassifier(max_depth= 8, min_child_weight= 4, gamma = 0.0, subsample= 0.9, colsample_bytree= 0.85,\n",
    "                    eta=0.3, objective='multi:softprob', \n",
    "                    num_class=8, early_stopping_rounds=20)\n",
    "\n",
    "eval_set = [(X_train_new, y_train), (X_test1, y_test1)]\n",
    "\n",
    "#We evaluate the model's performance on training and test sets by three evaluation metrics\n",
    "# https://github.com/dmlc/xgboost/blob/master/doc/parameter.md\n",
    "model.fit(X_train_new, y_train, eval_set = eval_set, eval_metric=[\"merror\", \"mlogloss\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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7Rwzby+t7bnzs+fD9JTDnOlj2ONw7G5b9HoLtPX/GGBPWLBSGoCk5STx5zRwa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\nhYIxxphO/x9UItfkm88zDwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a98a2a3f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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NRv63cAtvz88lOjKMoR2bc2aXlpzeuSXNrMRhTIPiT8niEeBrVX2vZkI6Play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\n/wfLTaIFetLKbgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a98d95ed68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "results = model.evals_result()\n",
    "epochs = len(results['validation_0']['merror'])\n",
    "x_axis = range(0, epochs)\n",
    "\n",
    "# plot log loss\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(x_axis, results['validation_0']['mlogloss'], label='Train')\n",
    "ax.plot(x_axis, results['validation_1']['mlogloss'], label='Test')\n",
    "ax.legend()\n",
    "plt.ylabel('Log Loss')\n",
    "plt.title('XGBoost Log Loss')\n",
    "plt.show()\n",
    "\n",
    "# plot classification error\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(x_axis, results['validation_0']['merror'], label='Train')\n",
    "ax.plot(x_axis, results['validation_1']['merror'], label='Test')\n",
    "ax.legend()\n",
    "plt.ylabel('Classification Error')\n",
    "plt.title('XGBoost Classification Error')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Tune reg_alpha"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Grid search took 9.831018881003063 minutes\n"
     ]
    }
   ],
   "source": [
    "param_grid6 = {'reg_alpha':[1e-5, 1e-2, 0.1, 1, 100]}\n",
    "\n",
    "start = time()\n",
    "\n",
    "grid_search6 = GridSearchCV(estimator = XGBClassifier(max_depth= 8, min_child_weight= 4, gamma = 0.0,\n",
    "                                                      subsample = 0.9, colsample_bytree = 0.85, \n",
    "                                                      eta=0.3, objective='multi:softprob', \n",
    "                                                      num_class=8, early_stopping_rounds=20), \n",
    "                       param_grid = param_grid6, scoring='accuracy',n_jobs=4)\n",
    "\n",
    "grid_search6.fit(X_train_new, y_train)\n",
    "print(\"Grid search took {} minutes\".format((time()-start)/60))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([mean: 0.50400, std: 0.00057, params: {'reg_alpha': 1e-05},\n",
       "  mean: 0.50400, std: 0.00051, params: {'reg_alpha': 0.01},\n",
       "  mean: 0.50276, std: 0.00169, params: {'reg_alpha': 0.1},\n",
       "  mean: 0.50376, std: 0.00103, params: {'reg_alpha': 1},\n",
       "  mean: 0.48825, std: 0.00087, params: {'reg_alpha': 100}],\n",
       " {'reg_alpha': 1e-05},\n",
       " 0.504)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search6.grid_scores_, grid_search6.best_params_, grid_search6.best_score_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Narrow down search space for reg_alpha"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Grid search took 8.637335697809855 minutes\n"
     ]
    }
   ],
   "source": [
    "param_grid7 = {'reg_alpha':[1e-7, 1e-6, 1e-5, 1e-4]}\n",
    "\n",
    "start = time()\n",
    "\n",
    "grid_search7 = GridSearchCV(estimator = XGBClassifier(max_depth= 8, min_child_weight= 4, gamma = 0.0,\n",
    "                                                      subsample = 0.9, colsample_bytree = 0.85, \n",
    "                                                      eta=0.3, objective='multi:softprob', \n",
    "                                                      num_class=8, early_stopping_rounds=20), \n",
    "                       param_grid = param_grid7, scoring='accuracy',n_jobs=4)\n",
    "\n",
    "grid_search7.fit(X_train_new, y_train)\n",
    "print(\"Grid search took {} minutes\".format((time()-start)/60))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([mean: 0.50400, std: 0.00057, params: {'reg_alpha': 1e-07},\n",
       "  mean: 0.50400, std: 0.00057, params: {'reg_alpha': 1e-06},\n",
       "  mean: 0.50400, std: 0.00057, params: {'reg_alpha': 1e-05},\n",
       "  mean: 0.50328, std: 0.00029, params: {'reg_alpha': 0.0001}],\n",
       " {'reg_alpha': 1e-07},\n",
       " 0.504)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search7.grid_scores_, grid_search7.best_params_, grid_search7.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Grid search took 8.397151724497478 minutes\n"
     ]
    }
   ],
   "source": [
    "param_grid7_1 = {'reg_alpha':[1e-9, 1e-8, 1e-7, 1e-6]}\n",
    "\n",
    "start = time()\n",
    "\n",
    "grid_search7_1 = GridSearchCV(estimator = XGBClassifier(max_depth= 8, min_child_weight= 4, gamma = 0.0,\n",
    "                                                      subsample = 0.9, colsample_bytree = 0.85, \n",
    "                                                      eta=0.3, objective='multi:softprob', \n",
    "                                                      num_class=8, early_stopping_rounds=20), \n",
    "                       param_grid = param_grid7_1, scoring='accuracy',n_jobs=4)\n",
    "\n",
    "grid_search7_1.fit(X_train_new, y_train)\n",
    "print(\"Grid search took {} minutes\".format((time()-start)/60))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([mean: 0.50400, std: 0.00057, params: {'reg_alpha': 1e-09},\n",
       "  mean: 0.50400, std: 0.00057, params: {'reg_alpha': 1e-08},\n",
       "  mean: 0.50400, std: 0.00057, params: {'reg_alpha': 1e-07},\n",
       "  mean: 0.50400, std: 0.00057, params: {'reg_alpha': 1e-06}],\n",
       " {'reg_alpha': 1e-09},\n",
       " 0.504)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search7_1.grid_scores_, grid_search7_1.best_params_, grid_search7_1.best_score_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### the score does not improve and remains at 50.4, hence stop further search for reg_alpha"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tune reg_lambda"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Grid search took 10.219808125495911 minutes\n"
     ]
    }
   ],
   "source": [
    "param_grid8 = {'reg_lambda':[1e-5, 1e-2, 0.1, 1, 100]}\n",
    "\n",
    "start = time()\n",
    "\n",
    "grid_search8 = GridSearchCV(estimator = XGBClassifier(max_depth= 8, min_child_weight= 4, gamma = 0.0,\n",
    "                                                      subsample = 0.9, colsample_bytree = 0.85, reg_alpha= 1e-9,\n",
    "                                                      eta=0.3, objective='multi:softprob', \n",
    "                                                      num_class=8, early_stopping_rounds=20), \n",
    "                       param_grid = param_grid8, scoring='accuracy',n_jobs=4)\n",
    "\n",
    "grid_search8.fit(X_train_new, y_train)\n",
    "print(\"Grid search took {} minutes\".format((time()-start)/60))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([mean: 0.50216, std: 0.00087, params: {'reg_lambda': 1e-05},\n",
       "  mean: 0.50254, std: 0.00156, params: {'reg_lambda': 0.01},\n",
       "  mean: 0.50123, std: 0.00143, params: {'reg_lambda': 0.1},\n",
       "  mean: 0.50400, std: 0.00057, params: {'reg_lambda': 1},\n",
       "  mean: 0.50075, std: 0.00170, params: {'reg_lambda': 100}],\n",
       " {'reg_lambda': 1},\n",
       " 0.504)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search8.grid_scores_, grid_search8.best_params_, grid_search8.best_score_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Narrow down search space for reg_lambda"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Grid search took 8.302910168965658 minutes\n"
     ]
    }
   ],
   "source": [
    "param_grid8_1 = {'reg_lambda':[0.9, 1, 2, 3]}\n",
    "\n",
    "start = time()\n",
    "\n",
    "grid_search8_1 = GridSearchCV(estimator = XGBClassifier(max_depth= 8, min_child_weight= 4, gamma = 0.0,\n",
    "                                                      subsample = 0.9, colsample_bytree = 0.85, reg_alpha= 1e-9,\n",
    "                                                      eta=0.3, objective='multi:softprob', \n",
    "                                                      num_class=8, early_stopping_rounds=20), \n",
    "                       param_grid = param_grid8_1, scoring='accuracy',n_jobs=4)\n",
    "\n",
    "grid_search8_1.fit(X_train_new, y_train)\n",
    "print(\"Grid search took {} minutes\".format((time()-start)/60))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([mean: 0.50260, std: 0.00093, params: {'reg_lambda': 0.9},\n",
       "  mean: 0.50400, std: 0.00057, params: {'reg_lambda': 1},\n",
       "  mean: 0.50171, std: 0.00131, params: {'reg_lambda': 2},\n",
       "  mean: 0.50430, std: 0.00154, params: {'reg_lambda': 3}],\n",
       " {'reg_lambda': 3},\n",
       " 0.5043)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search8_1.grid_scores_, grid_search8_1.best_params_, grid_search8_1.best_score_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Score imporves from 50.4 to 50.43, hence continue searching for reg_lambda"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Grid search took 11.91230006615321 minutes\n"
     ]
    }
   ],
   "source": [
    "param_grid8_2 = {'reg_lambda': list(range(3, 20, 3))}\n",
    "\n",
    "start = time()\n",
    "\n",
    "grid_search8_2 = GridSearchCV(estimator = XGBClassifier(max_depth= 8, min_child_weight= 4, gamma = 0.0,\n",
    "                                                      subsample = 0.9, colsample_bytree = 0.85, reg_alpha= 1e-9,\n",
    "                                                      eta=0.3, objective='multi:softprob', \n",
    "                                                      num_class=8, early_stopping_rounds=20), \n",
    "                       param_grid = param_grid8_2, scoring='accuracy',n_jobs=4)\n",
    "\n",
    "grid_search8_2.fit(X_train_new, y_train)\n",
    "print(\"Grid search took {} minutes\".format((time()-start)/60))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([mean: 0.50260, std: 0.00093, params: {'reg_lambda': 0.9},\n",
       "  mean: 0.50400, std: 0.00057, params: {'reg_lambda': 1},\n",
       "  mean: 0.50171, std: 0.00131, params: {'reg_lambda': 2},\n",
       "  mean: 0.50430, std: 0.00154, params: {'reg_lambda': 3}],\n",
       " {'reg_lambda': 3},\n",
       " 0.5043)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search8_1.grid_scores_, grid_search8_1.best_params_, grid_search8_1.best_score_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tune learning_rate \n",
    "guide: https://machinelearningmastery.com/tune-learning-rate-for-gradient-boosting-with-xgboost-in-python/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Grid search took 12.011093509197234 minutes\n"
     ]
    }
   ],
   "source": [
    "param_grid9 = {'learning_rate': [1e-4, 0.1e-3, 1e-2, 1e-1, 0.2, 0.3]}\n",
    "\n",
    "start = time()\n",
    "\n",
    "grid_search9 = GridSearchCV(estimator = XGBClassifier(max_depth= 8, min_child_weight= 4, gamma = 0.0, subsample = 0.9, \n",
    "                                                      colsample_bytree = 0.85, reg_alpha= 1e-9, reg_lambda= 3, \n",
    "                                                      objective='multi:softprob', \n",
    "                                                      num_class=8, early_stopping_rounds=20), \n",
    "                       param_grid = param_grid9, scoring='accuracy',n_jobs=4)\n",
    "\n",
    "grid_search9.fit(X_train_new, y_train)\n",
    "print(\"Grid search took {} minutes\".format((time()-start)/60))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([mean: 0.49074, std: 0.00143, params: {'learning_rate': 0.0001},\n",
       "  mean: 0.49074, std: 0.00143, params: {'learning_rate': 0.0001},\n",
       "  mean: 0.49486, std: 0.00051, params: {'learning_rate': 0.01},\n",
       "  mean: 0.50430, std: 0.00154, params: {'learning_rate': 0.1},\n",
       "  mean: 0.49940, std: 0.00172, params: {'learning_rate': 0.2},\n",
       "  mean: 0.49776, std: 0.00083, params: {'learning_rate': 0.3}],\n",
       " {'learning_rate': 0.1},\n",
       " 0.5043)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search9.grid_scores_, grid_search9.best_params_, grid_search9.best_score_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train classifier with optimum hyperparameter values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "       colsample_bytree=0.85, early_stopping_rounds=20, gamma=0.0,\n",
       "       learning_rate=0.1, max_delta_step=0, max_depth=8,\n",
       "       min_child_weight=4, missing=None, n_estimators=100, n_jobs=1,\n",
       "       nthread=None, num_class=8, objective='multi:softprob',\n",
       "       random_state=0, reg_alpha=1e-09, reg_lambda=3, scale_pos_weight=1,\n",
       "       seed=None, silent=True, subsample=0.9)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "\n",
    "model = XGBClassifier(max_depth= 8, min_child_weight= 4, gamma = 0.0, subsample = 0.9, \n",
    "                      colsample_bytree = 0.85, reg_alpha= 1e-9, reg_lambda= 3, learning_rate = 0.1,\n",
    "                      objective='multi:softprob', num_class=8, early_stopping_rounds=20)\n",
    "\n",
    "\n",
    "eval_set = [(X_train_new, y_train), (X_test1, y_test1)]\n",
    "model.fit(X_train_new, y_train, eval_set = eval_set, eval_metric=[\"merror\", \"mlogloss\"], verbose = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test the classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test accuracy at  -20 dB = 0.127\n",
      "Test accuracy at  -18 dB = 0.1235\n",
      "Test accuracy at  -16 dB = 0.12425\n",
      "Test accuracy at  -14 dB = 0.12875\n",
      "Test accuracy at  -12 dB = 0.14625\n",
      "Test accuracy at  -10 dB = 0.1915\n",
      "Test accuracy at  -8 dB = 0.286\n",
      "Test accuracy at  -6 dB = 0.36175\n",
      "Test accuracy at  -4 dB = 0.4055\n",
      "Test accuracy at  -2 dB = 0.452\n",
      "Test accuracy at  0 dB = 0.52375\n",
      "Test accuracy at  2 dB = 0.64625\n",
      "Test accuracy at  4 dB = 0.79425\n",
      "Test accuracy at  6 dB = 0.82325\n",
      "Test accuracy at  8 dB = 0.82825\n",
      "Test accuracy at  10 dB = 0.85275\n",
      "Test accuracy at  12 dB = 0.85175\n",
      "Test accuracy at  14 dB = 0.85775\n",
      "Test accuracy at  16 dB = 0.844\n",
      "Test accuracy at  18 dB = 0.8525\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "predictions = defaultdict(list)\n",
    "accuracy = defaultdict(list)\n",
    "y_pred = defaultdict(list)\n",
    "\n",
    "for snr in snrs:\n",
    "    y_pred[snr] = model.predict(X_test_new[snr])\n",
    "    predictions[snr] = [round(value) for value in y_pred[snr]]\n",
    "    accuracy[snr] = accuracy_score(y_test[snr], predictions[snr])\n",
    "    print (\"Test accuracy at \",snr,\"dB =\", accuracy[snr])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualize model performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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GMxYKrqljRrBdc6nfYyOQjDGhy0LBNSMnkfU6npiS9fbMZmNMyLJQcMVH+TkYM5Xo1ioo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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a9985fa0f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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KR/M30VAGezTG1F/+JIsTgBkiskpEFojIQhGpE8+WaIgiwsO4/ZTuvH/jUFrH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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a9985febe0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "results = model.evals_result()\n",
    "epochs = len(results['validation_0']['merror'])\n",
    "x_axis = range(0, epochs)\n",
    "\n",
    "# plot log loss\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(x_axis, results['validation_0']['mlogloss'], label='Train')\n",
    "ax.plot(x_axis, results['validation_1']['mlogloss'], label='Test')\n",
    "ax.legend()\n",
    "plt.ylabel('Log Loss')\n",
    "plt.title('XGBoost Log Loss')\n",
    "plt.show()\n",
    "\n",
    "# plot classification error\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(x_axis, results['validation_0']['merror'], label='Train')\n",
    "ax.plot(x_axis, results['validation_1']['merror'], label='Test')\n",
    "ax.legend()\n",
    "plt.ylabel('Classification Error')\n",
    "plt.title('XGBoost Classification Error')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Ki0VDLviqbCWE6lZIkMlkuHXrFs6ePYuff/5Zo8C1tbWt8q5k6h4fxQ0ODq70gqa2bdvC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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a99b304da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use('classic')\n",
    "acc_test = sorted(accuracy.items())\n",
    "new_acc = []\n",
    "for i in range(len(acc_test)):\n",
    "    new_acc.append(acc_test[i][1])\n",
    "acc_test_values = new_acc \n",
    "\n",
    "fig1 = plt.figure(figsize=(8,4), dpi=100)\n",
    "x = snrs\n",
    "y = list(acc_test_values)\n",
    "plt.plot(x, y, marker=\"o\", linewidth=2.0, linestyle='dashed', color='royalblue')\n",
    "plt.axis([-20, 20, 0, 1])\n",
    "plt.xticks(np.arange(min(x), max(x)+1, 2.0))\n",
    "plt.yticks(np.arange(0, 1, 0.10))\n",
    "\n",
    "ttl = plt.title('SNR vs Accuracy', fontsize=16)\n",
    "ttl.set_weight('bold')\n",
    "plt.xlabel('SNR (dB)', fontsize=14)\n",
    "plt.ylabel('Test accuracy', fontsize=14)\n",
    "plt.grid()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix\n",
      "       8PSK  BPSK  CPFSK  GFSK  PAM4  QAM16  QAM64  QPSK\n",
      "8PSK   0.86  0.00   0.00  0.00  0.00   0.06   0.07  0.01\n",
      "BPSK   0.00  0.97   0.00  0.00  0.02   0.00   0.00  0.01\n",
      "CPFSK  0.01  0.00   0.97  0.01  0.00   0.00   0.01  0.00\n",
      "GFSK   0.01  0.00   0.05  0.93  0.00   0.00   0.01  0.00\n",
      "PAM4   0.00  0.02   0.00  0.00  0.96   0.01   0.01  0.00\n",
      "QAM16  0.02  0.00   0.00  0.00  0.00   0.57   0.41  0.00\n",
      "QAM64  0.01  0.00   0.00  0.00  0.00   0.40   0.58  0.00\n",
      "QPSK   0.03  0.00   0.00  0.00  0.00   0.06   0.01  0.90\n"
     ]
    },
    {
     "data": {
      "image/png": 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x8VHn279/P/v371d/rVQqGThwIAMGDACKZo0uWrSIb775hsOHDxMVFUVUVBTL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\nV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Fwcq1evfmi7eYW4GwmC4onVp08fevXqRVNTE8OHD7/rtba2thQWFtLS0tJmpFVUVHTP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\n8qEkO8jKTrUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a99b3974e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Confusion Matrix\n",
    "    \n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "classes = ['8PSK', 'BPSK', 'CPFSK', 'GFSK', 'PAM4', 'QAM16', 'QAM64', 'QPSK']\n",
    "conf_matrix = confusion_matrix(y_pred[18], y_test[18])  \n",
    "\n",
    "conf_matrix = conf_matrix.astype('float') / conf_matrix.sum(axis=1)[:, np.newaxis]\n",
    "conf_matrix = conf_matrix.round(decimals = 2)\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "df = pd.DataFrame(data = conf_matrix, columns = classes, index = classes) \n",
    "print(\"Confusion Matrix\")\n",
    "print(df)\n",
    "\n",
    "fig1 = plt.figure(figsize=(8,4), dpi=100)\n",
    "plt.imshow(conf_matrix, interpolation = 'nearest', cmap = plt.cm.Purples)\n",
    "ticks = np.arange(len(classes))\n",
    "plt.title(\"Confusion Matrix\")\n",
    "plt.xticks(ticks, classes, rotation=45)\n",
    "plt.yticks(ticks, classes)\n",
    "\n",
    "plt.ylabel('True class')\n",
    "plt.xlabel('Predicted class')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.colorbar()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Use xbg library (rather than sklearn xgboost wrapper)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Form dmatrices required by xgb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dtrain = xgb.DMatrix(X_train_new, label=y_train)\n",
    "dtest = defaultdict(list)\n",
    "\n",
    "for snr in snrs:\n",
    "    dtest[snr] = xgb.DMatrix(X_test_new[snr], label=y_test[snr])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### n_boost_rounds = 100 = default # of estimators \n",
    "(num_boost_round = no. of trees to train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "       colsample_bytree=0.85, early_stopping_rounds=20, gamma=0.0,\n",
       "       learning_rate=0.1, max_delta_step=0, max_depth=8,\n",
       "       min_child_weight=4, missing=None, n_estimators=100, n_jobs=1,\n",
       "       nthread=None, num_class=8, objective='multi:softprob',\n",
       "       random_state=0, reg_alpha=1e-09, reg_lambda=3, scale_pos_weight=1,\n",
       "       seed=None, silent=True, subsample=0.9)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgbclassifier_model = model  #model whose hyperparameters we tuned\n",
    "parameters = xgbclassifier_model.get_xgb_params()\n",
    "\n",
    "#Use cross validation to set n_estimators \n",
    "eval_history = xgb.cv(parameters, dtrain, metrics = ['mlogloss', 'merror'], nfold = 5, early_stopping_rounds = 20,\n",
    "                         num_boost_round = xgbclassifier_model.get_params()['n_estimators'])\n",
    "xgbclassifier_model.set_params(n_estimators = eval_history.shape[0])\n",
    "\n",
    "xgbclassifier_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\tvalidation_0-mlogloss:1.94242\tvalidation_0-merror:0.49605\tvalidation_1-mlogloss:1.94496\tvalidation_1-merror:0.5125\n",
      "[1]\tvalidation_0-mlogloss:1.84592\tvalidation_0-merror:0.487038\tvalidation_1-mlogloss:1.85107\tvalidation_1-merror:0.50645\n",
      "[2]\tvalidation_0-mlogloss:1.76645\tvalidation_0-merror:0.481887\tvalidation_1-mlogloss:1.77405\tvalidation_1-merror:0.505188\n",
      "[3]\tvalidation_0-mlogloss:1.70012\tvalidation_0-merror:0.4801\tvalidation_1-mlogloss:1.70999\tvalidation_1-merror:0.504613\n",
      "[4]\tvalidation_0-mlogloss:1.64396\tvalidation_0-merror:0.478\tvalidation_1-mlogloss:1.65611\tvalidation_1-merror:0.503012\n",
      "[5]\tvalidation_0-mlogloss:1.59659\tvalidation_0-merror:0.4765\tvalidation_1-mlogloss:1.61064\tvalidation_1-merror:0.503287\n",
      "[6]\tvalidation_0-mlogloss:1.55416\tvalidation_0-merror:0.476088\tvalidation_1-mlogloss:1.57024\tvalidation_1-merror:0.502612\n",
      "[7]\tvalidation_0-mlogloss:1.5172\tvalidation_0-merror:0.474625\tvalidation_1-mlogloss:1.53524\tvalidation_1-merror:0.50195\n",
      "[8]\tvalidation_0-mlogloss:1.48358\tvalidation_0-merror:0.472338\tvalidation_1-mlogloss:1.50362\tvalidation_1-merror:0.500888\n",
      "[9]\tvalidation_0-mlogloss:1.45402\tvalidation_0-merror:0.47225\tvalidation_1-mlogloss:1.47588\tvalidation_1-merror:0.500825\n",
      "[10]\tvalidation_0-mlogloss:1.42713\tvalidation_0-merror:0.469737\tvalidation_1-mlogloss:1.45076\tvalidation_1-merror:0.499038\n",
      "[11]\tvalidation_0-mlogloss:1.40484\tvalidation_0-merror:0.468087\tvalidation_1-mlogloss:1.43017\tvalidation_1-merror:0.498825\n",
      "[12]\tvalidation_0-mlogloss:1.38281\tvalidation_0-merror:0.466487\tvalidation_1-mlogloss:1.41001\tvalidation_1-merror:0.497963\n",
      "[13]\tvalidation_0-mlogloss:1.36315\tvalidation_0-merror:0.465138\tvalidation_1-mlogloss:1.39211\tvalidation_1-merror:0.497725\n",
      "[14]\tvalidation_0-mlogloss:1.34591\tvalidation_0-merror:0.463625\tvalidation_1-mlogloss:1.37644\tvalidation_1-merror:0.497162\n",
      "[15]\tvalidation_0-mlogloss:1.32951\tvalidation_0-merror:0.46225\tvalidation_1-mlogloss:1.36166\tvalidation_1-merror:0.497238\n",
      "[16]\tvalidation_0-mlogloss:1.31396\tvalidation_0-merror:0.460063\tvalidation_1-mlogloss:1.34764\tvalidation_1-merror:0.49655\n",
      "[17]\tvalidation_0-mlogloss:1.30027\tvalidation_0-merror:0.459575\tvalidation_1-mlogloss:1.33535\tvalidation_1-merror:0.496\n",
      "[18]\tvalidation_0-mlogloss:1.28854\tvalidation_0-merror:0.458575\tvalidation_1-mlogloss:1.32499\tvalidation_1-merror:0.495775\n",
      "[19]\tvalidation_0-mlogloss:1.27675\tvalidation_0-merror:0.457687\tvalidation_1-mlogloss:1.31444\tvalidation_1-merror:0.49505\n",
      "[20]\tvalidation_0-mlogloss:1.26637\tvalidation_0-merror:0.456863\tvalidation_1-mlogloss:1.30532\tvalidation_1-merror:0.494287\n",
      "[21]\tvalidation_0-mlogloss:1.25644\tvalidation_0-merror:0.455638\tvalidation_1-mlogloss:1.29684\tvalidation_1-merror:0.494063\n",
      "[22]\tvalidation_0-mlogloss:1.24692\tvalidation_0-merror:0.454725\tvalidation_1-mlogloss:1.2887\tvalidation_1-merror:0.4946\n",
      "[23]\tvalidation_0-mlogloss:1.23835\tvalidation_0-merror:0.454125\tvalidation_1-mlogloss:1.28147\tvalidation_1-merror:0.494112\n",
      "[24]\tvalidation_0-mlogloss:1.23045\tvalidation_0-merror:0.453512\tvalidation_1-mlogloss:1.27487\tvalidation_1-merror:0.494013\n",
      "[25]\tvalidation_0-mlogloss:1.22314\tvalidation_0-merror:0.451425\tvalidation_1-mlogloss:1.26868\tvalidation_1-merror:0.493762\n",
      "[26]\tvalidation_0-mlogloss:1.21628\tvalidation_0-merror:0.45105\tvalidation_1-mlogloss:1.26309\tvalidation_1-merror:0.493275\n",
      "[27]\tvalidation_0-mlogloss:1.20994\tvalidation_0-merror:0.450588\tvalidation_1-mlogloss:1.25798\tvalidation_1-merror:0.493612\n",
      "[28]\tvalidation_0-mlogloss:1.20411\tvalidation_0-merror:0.449125\tvalidation_1-mlogloss:1.25334\tvalidation_1-merror:0.493612\n",
      "[29]\tvalidation_0-mlogloss:1.19847\tvalidation_0-merror:0.44785\tvalidation_1-mlogloss:1.24887\tvalidation_1-merror:0.493113\n",
      "[30]\tvalidation_0-mlogloss:1.19343\tvalidation_0-merror:0.447037\tvalidation_1-mlogloss:1.24506\tvalidation_1-merror:0.492825\n",
      "[31]\tvalidation_0-mlogloss:1.18859\tvalidation_0-merror:0.445962\tvalidation_1-mlogloss:1.24134\tvalidation_1-merror:0.492887\n",
      "[32]\tvalidation_0-mlogloss:1.18381\tvalidation_0-merror:0.445225\tvalidation_1-mlogloss:1.23771\tvalidation_1-merror:0.49275\n",
      "[33]\tvalidation_0-mlogloss:1.1793\tvalidation_0-merror:0.443812\tvalidation_1-mlogloss:1.23447\tvalidation_1-merror:0.493012\n",
      "[34]\tvalidation_0-mlogloss:1.17527\tvalidation_0-merror:0.443087\tvalidation_1-mlogloss:1.23152\tvalidation_1-merror:0.49275\n",
      "[35]\tvalidation_0-mlogloss:1.1716\tvalidation_0-merror:0.442425\tvalidation_1-mlogloss:1.22881\tvalidation_1-merror:0.491875\n",
      "[36]\tvalidation_0-mlogloss:1.16761\tvalidation_0-merror:0.44175\tvalidation_1-mlogloss:1.22611\tvalidation_1-merror:0.492088\n",
      "[37]\tvalidation_0-mlogloss:1.16422\tvalidation_0-merror:0.441475\tvalidation_1-mlogloss:1.22361\tvalidation_1-merror:0.491812\n",
      "[38]\tvalidation_0-mlogloss:1.16078\tvalidation_0-merror:0.441213\tvalidation_1-mlogloss:1.22114\tvalidation_1-merror:0.491275\n",
      "[39]\tvalidation_0-mlogloss:1.15736\tvalidation_0-merror:0.4404\tvalidation_1-mlogloss:1.21871\tvalidation_1-merror:0.491462\n",
      "[40]\tvalidation_0-mlogloss:1.15444\tvalidation_0-merror:0.439512\tvalidation_1-mlogloss:1.21675\tvalidation_1-merror:0.49135\n",
      "[41]\tvalidation_0-mlogloss:1.15165\tvalidation_0-merror:0.43915\tvalidation_1-mlogloss:1.2149\tvalidation_1-merror:0.491575\n",
      "[42]\tvalidation_0-mlogloss:1.14887\tvalidation_0-merror:0.438587\tvalidation_1-mlogloss:1.21314\tvalidation_1-merror:0.491125\n",
      "[43]\tvalidation_0-mlogloss:1.14626\tvalidation_0-merror:0.437675\tvalidation_1-mlogloss:1.21139\tvalidation_1-merror:0.4911\n",
      "[44]\tvalidation_0-mlogloss:1.14382\tvalidation_0-merror:0.436325\tvalidation_1-mlogloss:1.20992\tvalidation_1-merror:0.490575\n",
      "[45]\tvalidation_0-mlogloss:1.14141\tvalidation_0-merror:0.43535\tvalidation_1-mlogloss:1.20862\tvalidation_1-merror:0.491025\n",
      "[46]\tvalidation_0-mlogloss:1.13908\tvalidation_0-merror:0.433863\tvalidation_1-mlogloss:1.20727\tvalidation_1-merror:0.490687\n",
      "[47]\tvalidation_0-mlogloss:1.13711\tvalidation_0-merror:0.432175\tvalidation_1-mlogloss:1.2062\tvalidation_1-merror:0.490925\n",
      "[48]\tvalidation_0-mlogloss:1.13467\tvalidation_0-merror:0.431175\tvalidation_1-mlogloss:1.20498\tvalidation_1-merror:0.490862\n",
      "[49]\tvalidation_0-mlogloss:1.13241\tvalidation_0-merror:0.430737\tvalidation_1-mlogloss:1.20371\tvalidation_1-merror:0.490537\n",
      "[50]\tvalidation_0-mlogloss:1.13006\tvalidation_0-merror:0.428962\tvalidation_1-mlogloss:1.20254\tvalidation_1-merror:0.489962\n",
      "[51]\tvalidation_0-mlogloss:1.12784\tvalidation_0-merror:0.42825\tvalidation_1-mlogloss:1.20135\tvalidation_1-merror:0.4898\n",
      "[52]\tvalidation_0-mlogloss:1.12589\tvalidation_0-merror:0.426825\tvalidation_1-mlogloss:1.20049\tvalidation_1-merror:0.490012\n",
      "[53]\tvalidation_0-mlogloss:1.12395\tvalidation_0-merror:0.42585\tvalidation_1-mlogloss:1.19952\tvalidation_1-merror:0.490263\n",
      "[54]\tvalidation_0-mlogloss:1.12207\tvalidation_0-merror:0.425425\tvalidation_1-mlogloss:1.19858\tvalidation_1-merror:0.49025\n",
      "[55]\tvalidation_0-mlogloss:1.12018\tvalidation_0-merror:0.424462\tvalidation_1-mlogloss:1.19776\tvalidation_1-merror:0.49005\n",
      "[56]\tvalidation_0-mlogloss:1.11831\tvalidation_0-merror:0.423113\tvalidation_1-mlogloss:1.19684\tvalidation_1-merror:0.489713\n",
      "[57]\tvalidation_0-mlogloss:1.11677\tvalidation_0-merror:0.421762\tvalidation_1-mlogloss:1.19623\tvalidation_1-merror:0.489863\n",
      "[58]\tvalidation_0-mlogloss:1.11469\tvalidation_0-merror:0.4207\tvalidation_1-mlogloss:1.19553\tvalidation_1-merror:0.48955\n",
      "[59]\tvalidation_0-mlogloss:1.11311\tvalidation_0-merror:0.420087\tvalidation_1-mlogloss:1.19488\tvalidation_1-merror:0.489437\n",
      "[60]\tvalidation_0-mlogloss:1.11132\tvalidation_0-merror:0.418375\tvalidation_1-mlogloss:1.19436\tvalidation_1-merror:0.488512\n",
      "[61]\tvalidation_0-mlogloss:1.10988\tvalidation_0-merror:0.417412\tvalidation_1-mlogloss:1.19379\tvalidation_1-merror:0.488475\n",
      "[62]\tvalidation_0-mlogloss:1.10836\tvalidation_0-merror:0.416663\tvalidation_1-mlogloss:1.19322\tvalidation_1-merror:0.488313\n",
      "[63]\tvalidation_0-mlogloss:1.10674\tvalidation_0-merror:0.415237\tvalidation_1-mlogloss:1.19279\tvalidation_1-merror:0.48875\n",
      "[64]\tvalidation_0-mlogloss:1.10518\tvalidation_0-merror:0.4143\tvalidation_1-mlogloss:1.19229\tvalidation_1-merror:0.4886\n",
      "[65]\tvalidation_0-mlogloss:1.10342\tvalidation_0-merror:0.41335\tvalidation_1-mlogloss:1.19182\tvalidation_1-merror:0.489038\n",
      "[66]\tvalidation_0-mlogloss:1.10182\tvalidation_0-merror:0.411887\tvalidation_1-mlogloss:1.19143\tvalidation_1-merror:0.489388\n",
      "[67]\tvalidation_0-mlogloss:1.10028\tvalidation_0-merror:0.410925\tvalidation_1-mlogloss:1.19103\tvalidation_1-merror:0.489025\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[68]\tvalidation_0-mlogloss:1.09912\tvalidation_0-merror:0.410363\tvalidation_1-mlogloss:1.1907\tvalidation_1-merror:0.488687\n",
      "[69]\tvalidation_0-mlogloss:1.09788\tvalidation_0-merror:0.409362\tvalidation_1-mlogloss:1.19039\tvalidation_1-merror:0.488663\n",
      "[70]\tvalidation_0-mlogloss:1.09665\tvalidation_0-merror:0.409038\tvalidation_1-mlogloss:1.18992\tvalidation_1-merror:0.488613\n",
      "[71]\tvalidation_0-mlogloss:1.09513\tvalidation_0-merror:0.408237\tvalidation_1-mlogloss:1.18957\tvalidation_1-merror:0.488963\n",
      "[72]\tvalidation_0-mlogloss:1.09371\tvalidation_0-merror:0.407063\tvalidation_1-mlogloss:1.18925\tvalidation_1-merror:0.4891\n",
      "[73]\tvalidation_0-mlogloss:1.09244\tvalidation_0-merror:0.406\tvalidation_1-mlogloss:1.18897\tvalidation_1-merror:0.489062\n",
      "[74]\tvalidation_0-mlogloss:1.09063\tvalidation_0-merror:0.403488\tvalidation_1-mlogloss:1.18876\tvalidation_1-merror:0.489013\n",
      "[75]\tvalidation_0-mlogloss:1.0894\tvalidation_0-merror:0.402487\tvalidation_1-mlogloss:1.18851\tvalidation_1-merror:0.488525\n",
      "[76]\tvalidation_0-mlogloss:1.08797\tvalidation_0-merror:0.400862\tvalidation_1-mlogloss:1.18833\tvalidation_1-merror:0.488825\n",
      "[77]\tvalidation_0-mlogloss:1.08689\tvalidation_0-merror:0.399763\tvalidation_1-mlogloss:1.18818\tvalidation_1-merror:0.4889\n",
      "[78]\tvalidation_0-mlogloss:1.08543\tvalidation_0-merror:0.3981\tvalidation_1-mlogloss:1.18798\tvalidation_1-merror:0.489138\n",
      "[79]\tvalidation_0-mlogloss:1.08444\tvalidation_0-merror:0.3973\tvalidation_1-mlogloss:1.18777\tvalidation_1-merror:0.489312\n",
      "[80]\tvalidation_0-mlogloss:1.08301\tvalidation_0-merror:0.39575\tvalidation_1-mlogloss:1.18749\tvalidation_1-merror:0.4891\n",
      "[81]\tvalidation_0-mlogloss:1.08193\tvalidation_0-merror:0.394937\tvalidation_1-mlogloss:1.18725\tvalidation_1-merror:0.489062\n",
      "[82]\tvalidation_0-mlogloss:1.08092\tvalidation_0-merror:0.394662\tvalidation_1-mlogloss:1.18715\tvalidation_1-merror:0.488975\n",
      "[83]\tvalidation_0-mlogloss:1.07949\tvalidation_0-merror:0.392612\tvalidation_1-mlogloss:1.18699\tvalidation_1-merror:0.489563\n",
      "[84]\tvalidation_0-mlogloss:1.07814\tvalidation_0-merror:0.39125\tvalidation_1-mlogloss:1.18672\tvalidation_1-merror:0.489163\n",
      "[85]\tvalidation_0-mlogloss:1.07705\tvalidation_0-merror:0.390588\tvalidation_1-mlogloss:1.18658\tvalidation_1-merror:0.489175\n",
      "[86]\tvalidation_0-mlogloss:1.07614\tvalidation_0-merror:0.38955\tvalidation_1-mlogloss:1.18651\tvalidation_1-merror:0.489375\n",
      "[87]\tvalidation_0-mlogloss:1.07517\tvalidation_0-merror:0.388675\tvalidation_1-mlogloss:1.18643\tvalidation_1-merror:0.489475\n",
      "[88]\tvalidation_0-mlogloss:1.07395\tvalidation_0-merror:0.388088\tvalidation_1-mlogloss:1.18627\tvalidation_1-merror:0.48945\n",
      "[89]\tvalidation_0-mlogloss:1.07269\tvalidation_0-merror:0.387575\tvalidation_1-mlogloss:1.18616\tvalidation_1-merror:0.489275\n",
      "[90]\tvalidation_0-mlogloss:1.07143\tvalidation_0-merror:0.38625\tvalidation_1-mlogloss:1.18599\tvalidation_1-merror:0.48915\n",
      "[91]\tvalidation_0-mlogloss:1.07021\tvalidation_0-merror:0.384887\tvalidation_1-mlogloss:1.18589\tvalidation_1-merror:0.489225\n",
      "[92]\tvalidation_0-mlogloss:1.06905\tvalidation_0-merror:0.38335\tvalidation_1-mlogloss:1.18577\tvalidation_1-merror:0.4893\n",
      "[93]\tvalidation_0-mlogloss:1.0677\tvalidation_0-merror:0.381425\tvalidation_1-mlogloss:1.1856\tvalidation_1-merror:0.489013\n",
      "[94]\tvalidation_0-mlogloss:1.06641\tvalidation_0-merror:0.380313\tvalidation_1-mlogloss:1.18556\tvalidation_1-merror:0.488975\n",
      "[95]\tvalidation_0-mlogloss:1.06513\tvalidation_0-merror:0.378888\tvalidation_1-mlogloss:1.1854\tvalidation_1-merror:0.4887\n",
      "[96]\tvalidation_0-mlogloss:1.06402\tvalidation_0-merror:0.377212\tvalidation_1-mlogloss:1.18541\tvalidation_1-merror:0.489038\n",
      "[97]\tvalidation_0-mlogloss:1.06204\tvalidation_0-merror:0.374512\tvalidation_1-mlogloss:1.18523\tvalidation_1-merror:0.488788\n",
      "[98]\tvalidation_0-mlogloss:1.06113\tvalidation_0-merror:0.373937\tvalidation_1-mlogloss:1.18524\tvalidation_1-merror:0.488712\n",
      "[99]\tvalidation_0-mlogloss:1.05985\tvalidation_0-merror:0.372613\tvalidation_1-mlogloss:1.18519\tvalidation_1-merror:0.48895\n",
      "Fitting model to data took 2.981314512093862 minutes\n"
     ]
    }
   ],
   "source": [
    "eval_set = [(X_train_new, y_train), (X_test1, y_test1)]\n",
    "start = time()\n",
    "xgbclassifier_model.fit(X_train_new, y_train, eval_metric = ['mlogloss','merror'], \n",
    "                        eval_set = eval_set) #fit model to data\n",
    "print(\"Fitting model to data took {} minutes\".format((time() - start)/60))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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FFOwpoPeTvXnpq5d0kUARiRi1JKJs+XIYORJuvdU7Q/tQZq+fzQ/f+CGZTTP5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\nEiIiEpRCQkREglJIiIhIUGE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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a99b2cb198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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/EIsbpPAgIICqakJlL1qVQpU8ALZvhz/8AS6/HG67LSQhGGNMpRxp8ih3VF1V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\nGQilQz8CiQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a9985d4ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "results_xgbclassifier = xgbclassifier_model.evals_result()\n",
    "epochs = len(results_xgbclassifier_xgbclassifier['validation_0']['merror'])\n",
    "x_axis = range(0, epochs)\n",
    "\n",
    "# plot log loss\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(x_axis, results_xgbclassifier['validation_0']['mlogloss'], label='Train')\n",
    "ax.plot(x_axis, results_xgbclassifier['validation_1']['mlogloss'], label='Test')\n",
    "ax.legend()\n",
    "plt.ylabel('Log Loss')\n",
    "plt.title('XGBoost Log Loss')\n",
    "plt.show()\n",
    "\n",
    "# plot classification error\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(x_axis, results_xgbclassifier['validation_0']['merror'], label='Train')\n",
    "ax.plot(x_axis, results_xgbclassifier['validation_1']['merror'], label='Test')\n",
    "ax.legend()\n",
    "plt.ylabel('Classification Error')\n",
    "plt.title('XGBoost Classification Error')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test accuracy at  -20 dB = 0.1255\n",
      "Test accuracy at  -18 dB = 0.12325\n",
      "Test accuracy at  -16 dB = 0.1225\n",
      "Test accuracy at  -14 dB = 0.1295\n",
      "Test accuracy at  -12 dB = 0.1405\n",
      "Test accuracy at  -10 dB = 0.1895\n",
      "Test accuracy at  -8 dB = 0.25975\n",
      "Test accuracy at  -6 dB = 0.34725\n",
      "Test accuracy at  -4 dB = 0.40125\n",
      "Test accuracy at  -2 dB = 0.45375\n",
      "Test accuracy at  0 dB = 0.531\n",
      "Test accuracy at  2 dB = 0.65\n",
      "Test accuracy at  4 dB = 0.7845\n",
      "Test accuracy at  6 dB = 0.81925\n",
      "Test accuracy at  8 dB = 0.82875\n",
      "Test accuracy at  10 dB = 0.852\n",
      "Test accuracy at  12 dB = 0.8525\n",
      "Test accuracy at  14 dB = 0.85425\n",
      "Test accuracy at  16 dB = 0.84275\n",
      "Test accuracy at  18 dB = 0.848\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "predictions = defaultdict(list)\n",
    "accuracy = defaultdict(list)\n",
    "y_pred = defaultdict(list)\n",
    "\n",
    "for snr in snrs:\n",
    "    y_pred[snr] = xgbclassifier_model.predict(X_test_new[snr])\n",
    "    predictions[snr] = [round(value) for value in y_pred[snr]]\n",
    "    accuracy[snr] = accuracy_score(y_test[snr], predictions[snr])\n",
    "    print (\"Test accuracy at \",snr,\"dB =\", accuracy[snr])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ooKJYNOSGr6pmQqhphgSZTIabN2/i7NmzOHPmjEaBa2trW+2qZOoevorr5+dX5Q1NXbp0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\nIiIiIpJ3/gX1lTV2/SRNVgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a98d942dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use('classic')\n",
    "acc_test = sorted(accuracy.items())\n",
    "new_acc = []\n",
    "for i in range(len(acc_test)):\n",
    "    new_acc.append(acc_test[i][1])\n",
    "acc_test_values = new_acc \n",
    "\n",
    "fig1 = plt.figure(figsize=(8,4), dpi=100)\n",
    "x = snrs\n",
    "y = list(acc_test_values)\n",
    "plt.plot(x, y, marker=\"o\", linewidth=2.0, linestyle='dashed', color='royalblue')\n",
    "plt.axis([-20, 20, 0, 1])\n",
    "plt.xticks(np.arange(min(x), max(x)+1, 2.0))\n",
    "plt.yticks(np.arange(0, 1, 0.10))\n",
    "\n",
    "ttl = plt.title('SNR vs Accuracy', fontsize=16)\n",
    "ttl.set_weight('bold')\n",
    "plt.xlabel('SNR (dB)', fontsize=14)\n",
    "plt.ylabel('Test accuracy', fontsize=14)\n",
    "plt.grid()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix\n",
      "       8PSK  BPSK  CPFSK  GFSK  PAM4  QAM16  QAM64  QPSK\n",
      "8PSK   0.86  0.00   0.00  0.00  0.00   0.06   0.07  0.01\n",
      "BPSK   0.00  0.97   0.00  0.00  0.02   0.00   0.01  0.01\n",
      "CPFSK  0.01  0.00   0.98  0.01  0.00   0.00   0.00  0.00\n",
      "GFSK   0.01  0.00   0.04  0.94  0.00   0.00   0.01  0.00\n",
      "PAM4   0.00  0.02   0.00  0.00  0.96   0.01   0.01  0.00\n",
      "QAM16  0.02  0.00   0.00  0.00  0.00   0.55   0.44  0.00\n",
      "QAM64  0.01  0.00   0.00  0.00  0.00   0.43   0.55  0.00\n",
      "QPSK   0.03  0.00   0.00  0.00  0.00   0.05   0.01  0.90\n"
     ]
    },
    {
     "data": {
      "image/png": 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KVq9erV8neQqCoLu7u8bEGD8/P15//XVWrFhBly5dsLCwAKBbt24MGTIEgHr1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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a99fc5cfd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Confusion Matrix\n",
    "    \n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "classes = ['8PSK', 'BPSK', 'CPFSK', 'GFSK', 'PAM4', 'QAM16', 'QAM64', 'QPSK']\n",
    "conf_matrix = confusion_matrix(y_pred[18], y_test[18])  \n",
    "\n",
    "conf_matrix = conf_matrix.astype('float') / conf_matrix.sum(axis=1)[:, np.newaxis]\n",
    "conf_matrix = conf_matrix.round(decimals = 2)\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "df = pd.DataFrame(data = conf_matrix, columns = classes, index = classes) \n",
    "print(\"Confusion Matrix\")\n",
    "print(df)\n",
    "\n",
    "fig1 = plt.figure(figsize=(8,4), dpi=100)\n",
    "plt.imshow(conf_matrix, interpolation = 'nearest', cmap = plt.cm.Purples)\n",
    "ticks = np.arange(len(classes))\n",
    "plt.title(\"Confusion Matrix\")\n",
    "plt.xticks(ticks, classes, rotation=45)\n",
    "plt.yticks(ticks, classes)\n",
    "\n",
    "plt.ylabel('True class')\n",
    "plt.xlabel('Predicted class')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.colorbar()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### num_boost_round = 300; n_estimators = 300"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\tvalidation_0-mlogloss:1.94242\tvalidation_0-merror:0.49605\tvalidation_1-mlogloss:1.94496\tvalidation_1-merror:0.5125\n",
      "[1]\tvalidation_0-mlogloss:1.84592\tvalidation_0-merror:0.487038\tvalidation_1-mlogloss:1.85107\tvalidation_1-merror:0.50645\n",
      "[2]\tvalidation_0-mlogloss:1.76645\tvalidation_0-merror:0.481887\tvalidation_1-mlogloss:1.77405\tvalidation_1-merror:0.505188\n",
      "[3]\tvalidation_0-mlogloss:1.70012\tvalidation_0-merror:0.4801\tvalidation_1-mlogloss:1.70999\tvalidation_1-merror:0.504613\n",
      "[4]\tvalidation_0-mlogloss:1.64396\tvalidation_0-merror:0.478\tvalidation_1-mlogloss:1.65611\tvalidation_1-merror:0.503012\n",
      "[5]\tvalidation_0-mlogloss:1.59659\tvalidation_0-merror:0.4765\tvalidation_1-mlogloss:1.61064\tvalidation_1-merror:0.503287\n",
      "[6]\tvalidation_0-mlogloss:1.55416\tvalidation_0-merror:0.476088\tvalidation_1-mlogloss:1.57024\tvalidation_1-merror:0.502612\n",
      "[7]\tvalidation_0-mlogloss:1.5172\tvalidation_0-merror:0.474625\tvalidation_1-mlogloss:1.53524\tvalidation_1-merror:0.50195\n",
      "[8]\tvalidation_0-mlogloss:1.48358\tvalidation_0-merror:0.472338\tvalidation_1-mlogloss:1.50362\tvalidation_1-merror:0.500888\n",
      "[9]\tvalidation_0-mlogloss:1.45402\tvalidation_0-merror:0.47225\tvalidation_1-mlogloss:1.47588\tvalidation_1-merror:0.500825\n",
      "[10]\tvalidation_0-mlogloss:1.42713\tvalidation_0-merror:0.469737\tvalidation_1-mlogloss:1.45076\tvalidation_1-merror:0.499038\n",
      "[11]\tvalidation_0-mlogloss:1.40484\tvalidation_0-merror:0.468087\tvalidation_1-mlogloss:1.43017\tvalidation_1-merror:0.498825\n",
      "[12]\tvalidation_0-mlogloss:1.38281\tvalidation_0-merror:0.466487\tvalidation_1-mlogloss:1.41001\tvalidation_1-merror:0.497963\n",
      "[13]\tvalidation_0-mlogloss:1.36315\tvalidation_0-merror:0.465138\tvalidation_1-mlogloss:1.39211\tvalidation_1-merror:0.497725\n",
      "[14]\tvalidation_0-mlogloss:1.34591\tvalidation_0-merror:0.463625\tvalidation_1-mlogloss:1.37644\tvalidation_1-merror:0.497162\n",
      "[15]\tvalidation_0-mlogloss:1.32951\tvalidation_0-merror:0.46225\tvalidation_1-mlogloss:1.36166\tvalidation_1-merror:0.497238\n",
      "[16]\tvalidation_0-mlogloss:1.31396\tvalidation_0-merror:0.460063\tvalidation_1-mlogloss:1.34764\tvalidation_1-merror:0.49655\n",
      "[17]\tvalidation_0-mlogloss:1.30027\tvalidation_0-merror:0.459575\tvalidation_1-mlogloss:1.33535\tvalidation_1-merror:0.496\n",
      "[18]\tvalidation_0-mlogloss:1.28854\tvalidation_0-merror:0.458575\tvalidation_1-mlogloss:1.32499\tvalidation_1-merror:0.495775\n",
      "[19]\tvalidation_0-mlogloss:1.27675\tvalidation_0-merror:0.457687\tvalidation_1-mlogloss:1.31444\tvalidation_1-merror:0.49505\n",
      "[20]\tvalidation_0-mlogloss:1.26637\tvalidation_0-merror:0.456863\tvalidation_1-mlogloss:1.30532\tvalidation_1-merror:0.494287\n",
      "[21]\tvalidation_0-mlogloss:1.25644\tvalidation_0-merror:0.455638\tvalidation_1-mlogloss:1.29684\tvalidation_1-merror:0.494063\n",
      "[22]\tvalidation_0-mlogloss:1.24692\tvalidation_0-merror:0.454725\tvalidation_1-mlogloss:1.2887\tvalidation_1-merror:0.4946\n",
      "[23]\tvalidation_0-mlogloss:1.23835\tvalidation_0-merror:0.454125\tvalidation_1-mlogloss:1.28147\tvalidation_1-merror:0.494112\n",
      "[24]\tvalidation_0-mlogloss:1.23045\tvalidation_0-merror:0.453512\tvalidation_1-mlogloss:1.27487\tvalidation_1-merror:0.494013\n",
      "[25]\tvalidation_0-mlogloss:1.22314\tvalidation_0-merror:0.451425\tvalidation_1-mlogloss:1.26868\tvalidation_1-merror:0.493762\n",
      "[26]\tvalidation_0-mlogloss:1.21628\tvalidation_0-merror:0.45105\tvalidation_1-mlogloss:1.26309\tvalidation_1-merror:0.493275\n",
      "[27]\tvalidation_0-mlogloss:1.20994\tvalidation_0-merror:0.450588\tvalidation_1-mlogloss:1.25798\tvalidation_1-merror:0.493612\n",
      "[28]\tvalidation_0-mlogloss:1.20411\tvalidation_0-merror:0.449125\tvalidation_1-mlogloss:1.25334\tvalidation_1-merror:0.493612\n",
      "[29]\tvalidation_0-mlogloss:1.19847\tvalidation_0-merror:0.44785\tvalidation_1-mlogloss:1.24887\tvalidation_1-merror:0.493113\n",
      "[30]\tvalidation_0-mlogloss:1.19343\tvalidation_0-merror:0.447037\tvalidation_1-mlogloss:1.24506\tvalidation_1-merror:0.492825\n",
      "[31]\tvalidation_0-mlogloss:1.18859\tvalidation_0-merror:0.445962\tvalidation_1-mlogloss:1.24134\tvalidation_1-merror:0.492887\n",
      "[32]\tvalidation_0-mlogloss:1.18381\tvalidation_0-merror:0.445225\tvalidation_1-mlogloss:1.23771\tvalidation_1-merror:0.49275\n",
      "[33]\tvalidation_0-mlogloss:1.1793\tvalidation_0-merror:0.443812\tvalidation_1-mlogloss:1.23447\tvalidation_1-merror:0.493012\n",
      "[34]\tvalidation_0-mlogloss:1.17527\tvalidation_0-merror:0.443087\tvalidation_1-mlogloss:1.23152\tvalidation_1-merror:0.49275\n",
      "[35]\tvalidation_0-mlogloss:1.1716\tvalidation_0-merror:0.442425\tvalidation_1-mlogloss:1.22881\tvalidation_1-merror:0.491875\n",
      "[36]\tvalidation_0-mlogloss:1.16761\tvalidation_0-merror:0.44175\tvalidation_1-mlogloss:1.22611\tvalidation_1-merror:0.492088\n",
      "[37]\tvalidation_0-mlogloss:1.16422\tvalidation_0-merror:0.441475\tvalidation_1-mlogloss:1.22361\tvalidation_1-merror:0.491812\n",
      "[38]\tvalidation_0-mlogloss:1.16078\tvalidation_0-merror:0.441213\tvalidation_1-mlogloss:1.22114\tvalidation_1-merror:0.491275\n",
      "[39]\tvalidation_0-mlogloss:1.15736\tvalidation_0-merror:0.4404\tvalidation_1-mlogloss:1.21871\tvalidation_1-merror:0.491462\n",
      "[40]\tvalidation_0-mlogloss:1.15444\tvalidation_0-merror:0.439512\tvalidation_1-mlogloss:1.21675\tvalidation_1-merror:0.49135\n",
      "[41]\tvalidation_0-mlogloss:1.15165\tvalidation_0-merror:0.43915\tvalidation_1-mlogloss:1.2149\tvalidation_1-merror:0.491575\n",
      "[42]\tvalidation_0-mlogloss:1.14887\tvalidation_0-merror:0.438587\tvalidation_1-mlogloss:1.21314\tvalidation_1-merror:0.491125\n",
      "[43]\tvalidation_0-mlogloss:1.14626\tvalidation_0-merror:0.437675\tvalidation_1-mlogloss:1.21139\tvalidation_1-merror:0.4911\n",
      "[44]\tvalidation_0-mlogloss:1.14382\tvalidation_0-merror:0.436325\tvalidation_1-mlogloss:1.20992\tvalidation_1-merror:0.490575\n",
      "[45]\tvalidation_0-mlogloss:1.14141\tvalidation_0-merror:0.43535\tvalidation_1-mlogloss:1.20862\tvalidation_1-merror:0.491025\n",
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      "[295]\tvalidation_0-mlogloss:0.850963\tvalidation_0-merror:0.184425\tvalidation_1-mlogloss:1.18829\tvalidation_1-merror:0.491925\n",
      "[296]\tvalidation_0-mlogloss:0.849896\tvalidation_0-merror:0.183612\tvalidation_1-mlogloss:1.18833\tvalidation_1-merror:0.49195\n",
      "[297]\tvalidation_0-mlogloss:0.849118\tvalidation_0-merror:0.1831\tvalidation_1-mlogloss:1.18837\tvalidation_1-merror:0.492013\n",
      "[298]\tvalidation_0-mlogloss:0.848336\tvalidation_0-merror:0.1822\tvalidation_1-mlogloss:1.18839\tvalidation_1-merror:0.492238\n",
      "[299]\tvalidation_0-mlogloss:0.847657\tvalidation_0-merror:0.18175\tvalidation_1-mlogloss:1.18839\tvalidation_1-merror:0.492213\n",
      "Cross validation and fitting took 23.345912408828735 minutes\n"
     ]
    }
   ],
   "source": [
    "xgbclassifier_model2 = model  #model whose hyperparameters we tuned\n",
    "parameters = xgbclassifier_model2.get_xgb_params()\n",
    "\n",
    "#Use cross validation to set n_estimators\n",
    "start = time()\n",
    "eval_history = xgb.cv(parameters, dtrain, metrics = ['mlogloss', 'merror'], nfold = 5, early_stopping_rounds = 20,\n",
    "                         num_boost_round = 300)\n",
    "xgbclassifier_model2.set_params(n_estimators = 300)\n",
    "\n",
    "eval_set = [(X_train_new, y_train), (X_test1, y_test1)]\n",
    "\n",
    "xgbclassifier_model2.fit(X_train_new, y_train, eval_metric = ['mlogloss','merror'], \n",
    "                        eval_set = eval_set) #fit model to data\n",
    "print(\"Cross validation and fitting took {} minutes\".format((time() - start)/60))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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U/rdxHHen0iup9Eqc8L5qXdXnutYdyz7peOwjfa/ex06zMlV4xaF9Kr2SisrD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\nlCRERCSh/w+PLGmt3qrR2QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a99fb5b710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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l3HEHzJ3r/h0zBqTOD6jGmHjTEO95fKWqB9f1BJFmyaNm8+e7MUTat4drr3Xd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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a99e462da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "results_xgbclassifier2 = xgbclassifier_model2.evals_result()\n",
    "epochs = len(results_xgbclassifier2['validation_0']['merror'])\n",
    "x_axis = range(0, epochs)\n",
    "\n",
    "# plot log loss\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(x_axis, results_xgbclassifier2['validation_0']['mlogloss'], label='Train')\n",
    "ax.plot(x_axis, results_xgbclassifier2['validation_1']['mlogloss'], label='Test')\n",
    "ax.legend()\n",
    "plt.ylabel('Log Loss')\n",
    "plt.title('XGBoost Log Loss')\n",
    "plt.show()\n",
    "\n",
    "# plot classification error\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(x_axis, results_xgbclassifier2['validation_0']['merror'], label='Train')\n",
    "ax.plot(x_axis, results_xgbclassifier2['validation_1']['merror'], label='Test')\n",
    "ax.legend()\n",
    "plt.ylabel('Classification Error')\n",
    "plt.title('XGBoost Classification Error')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test accuracy at  -20 dB = 0.1255\n",
      "Test accuracy at  -18 dB = 0.12325\n",
      "Test accuracy at  -16 dB = 0.1225\n",
      "Test accuracy at  -14 dB = 0.1295\n",
      "Test accuracy at  -12 dB = 0.1405\n",
      "Test accuracy at  -10 dB = 0.1895\n",
      "Test accuracy at  -8 dB = 0.25975\n",
      "Test accuracy at  -6 dB = 0.34725\n",
      "Test accuracy at  -4 dB = 0.40125\n",
      "Test accuracy at  -2 dB = 0.45375\n",
      "Test accuracy at  0 dB = 0.531\n",
      "Test accuracy at  2 dB = 0.65\n",
      "Test accuracy at  4 dB = 0.7845\n",
      "Test accuracy at  6 dB = 0.81925\n",
      "Test accuracy at  8 dB = 0.82875\n",
      "Test accuracy at  10 dB = 0.852\n",
      "Test accuracy at  12 dB = 0.8525\n",
      "Test accuracy at  14 dB = 0.85425\n",
      "Test accuracy at  16 dB = 0.84275\n",
      "Test accuracy at  18 dB = 0.848\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "predictions = defaultdict(list)\n",
    "accuracy = defaultdict(list)\n",
    "y_pred = defaultdict(list)\n",
    "\n",
    "for snr in snrs:\n",
    "    y_pred[snr] = xgbclassifier_model2.predict(X_test_new[snr])\n",
    "    predictions[snr] = [round(value) for value in y_pred[snr]]\n",
    "    accuracy[snr] = accuracy_score(y_test[snr], predictions[snr])\n",
    "    print (\"Test accuracy at \",snr,\"dB =\", accuracy[snr])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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j+7HHHqvT8Q9yy/FJZJbO50QiwLu1DfILFQBs8FzgQI1iz7lFT9W+uWlH8cJw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\nIiIiIpJ3/gX1lTV2/SRNVgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a99e456a20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use('classic')\n",
    "acc_test = sorted(accuracy.items())\n",
    "new_acc = []\n",
    "for i in range(len(acc_test)):\n",
    "    new_acc.append(acc_test[i][1])\n",
    "acc_test_values = new_acc \n",
    "\n",
    "fig1 = plt.figure(figsize=(8,4), dpi=100)\n",
    "x = snrs\n",
    "y = list(acc_test_values)\n",
    "plt.plot(x, y, marker=\"o\", linewidth=2.0, linestyle='dashed', color='royalblue')\n",
    "plt.axis([-20, 20, 0, 1])\n",
    "plt.xticks(np.arange(min(x), max(x)+1, 2.0))\n",
    "plt.yticks(np.arange(0, 1, 0.10))\n",
    "\n",
    "ttl = plt.title('SNR vs Accuracy', fontsize=16)\n",
    "ttl.set_weight('bold')\n",
    "plt.xlabel('SNR (dB)', fontsize=14)\n",
    "plt.ylabel('Test accuracy', fontsize=14)\n",
    "plt.grid()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix\n",
      "       8PSK  BPSK  CPFSK  GFSK  PAM4  QAM16  QAM64  QPSK\n",
      "8PSK   0.86  0.00   0.00  0.00  0.00   0.06   0.07  0.01\n",
      "BPSK   0.00  0.97   0.00  0.00  0.02   0.00   0.01  0.01\n",
      "CPFSK  0.01  0.00   0.98  0.01  0.00   0.00   0.00  0.00\n",
      "GFSK   0.01  0.00   0.04  0.94  0.00   0.00   0.01  0.00\n",
      "PAM4   0.00  0.02   0.00  0.00  0.96   0.01   0.01  0.00\n",
      "QAM16  0.02  0.00   0.00  0.00  0.00   0.55   0.44  0.00\n",
      "QAM64  0.01  0.00   0.00  0.00  0.00   0.43   0.55  0.00\n",
      "QPSK   0.03  0.00   0.00  0.00  0.00   0.05   0.01  0.90\n"
     ]
    },
    {
     "data": {
      "image/png": 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KVq9erV8neQqCoLu7u8bEGD8/P15//XVWrFhBly5dsLCwAKBbt24MGTIEgHr1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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a99b2f1278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Confusion Matrix\n",
    "    \n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "classes = ['8PSK', 'BPSK', 'CPFSK', 'GFSK', 'PAM4', 'QAM16', 'QAM64', 'QPSK']\n",
    "conf_matrix = confusion_matrix(y_pred[18], y_test[18])  \n",
    "\n",
    "conf_matrix = conf_matrix.astype('float') / conf_matrix.sum(axis=1)[:, np.newaxis]\n",
    "conf_matrix = conf_matrix.round(decimals = 2)\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "df = pd.DataFrame(data = conf_matrix, columns = classes, index = classes) \n",
    "print(\"Confusion Matrix\")\n",
    "print(df)\n",
    "\n",
    "fig1 = plt.figure(figsize=(8,4), dpi=100)\n",
    "plt.imshow(conf_matrix, interpolation = 'nearest', cmap = plt.cm.Purples)\n",
    "ticks = np.arange(len(classes))\n",
    "plt.title(\"Confusion Matrix\")\n",
    "plt.xticks(ticks, classes, rotation=45)\n",
    "plt.yticks(ticks, classes)\n",
    "\n",
    "plt.ylabel('True class')\n",
    "plt.xlabel('Predicted class')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.colorbar()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tune trade-off between learning_rate and num_boost_round\n",
    "Low learning_rate implies more trees must be added to the ensemble; learning_rate is commonly set between 0.1 and 0.3\n",
    "and also less than 0.1. Our learning_rate of 0.1 found via grid search is high and suggests that the default no. of trees 100 (during grid search) is low and more trees should be added. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Grid search took 103.06827054023742 minutes\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "([mean: 0.49074, std: 0.00143, params: {'n_estimators': 100, 'learning_rate': 0.0001},\n",
       "  mean: 0.49074, std: 0.00178, params: {'n_estimators': 200, 'learning_rate': 0.0001},\n",
       "  mean: 0.49054, std: 0.00184, params: {'n_estimators': 300, 'learning_rate': 0.0001},\n",
       "  mean: 0.49075, std: 0.00196, params: {'n_estimators': 400, 'learning_rate': 0.0001},\n",
       "  mean: 0.49104, std: 0.00181, params: {'n_estimators': 500, 'learning_rate': 0.0001},\n",
       "  mean: 0.49107, std: 0.00162, params: {'n_estimators': 100, 'learning_rate': 0.001},\n",
       "  mean: 0.49151, std: 0.00143, params: {'n_estimators': 200, 'learning_rate': 0.001},\n",
       "  mean: 0.49220, std: 0.00131, params: {'n_estimators': 300, 'learning_rate': 0.001},\n",
       "  mean: 0.49302, std: 0.00166, params: {'n_estimators': 400, 'learning_rate': 0.001},\n",
       "  mean: 0.49379, std: 0.00142, params: {'n_estimators': 500, 'learning_rate': 0.001},\n",
       "  mean: 0.49486, std: 0.00051, params: {'n_estimators': 100, 'learning_rate': 0.01},\n",
       "  mean: 0.49855, std: 0.00061, params: {'n_estimators': 200, 'learning_rate': 0.01},\n",
       "  mean: 0.49985, std: 0.00126, params: {'n_estimators': 300, 'learning_rate': 0.01},\n",
       "  mean: 0.50081, std: 0.00116, params: {'n_estimators': 400, 'learning_rate': 0.01},\n",
       "  mean: 0.50169, std: 0.00140, params: {'n_estimators': 500, 'learning_rate': 0.01},\n",
       "  mean: 0.50430, std: 0.00154, params: {'n_estimators': 100, 'learning_rate': 0.1},\n",
       "  mean: 0.50091, std: 0.00159, params: {'n_estimators': 200, 'learning_rate': 0.1},\n",
       "  mean: 0.49860, std: 0.00142, params: {'n_estimators': 300, 'learning_rate': 0.1},\n",
       "  mean: 0.49843, std: 0.00068, params: {'n_estimators': 400, 'learning_rate': 0.1},\n",
       "  mean: 0.49717, std: 0.00133, params: {'n_estimators': 500, 'learning_rate': 0.1}],\n",
       " {'learning_rate': 0.1, 'n_estimators': 100},\n",
       " 0.5043)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_grid10 = {'learning_rate': [0.0001, 0.001, 0.01, 0.1], 'n_estimators': [100, 200, 300, 400, 500]}\n",
    "\n",
    "start = time()\n",
    "\n",
    "grid_search10 = GridSearchCV(estimator = XGBClassifier(max_depth= 8, min_child_weight= 4, gamma = 0.0, subsample = 0.9, \n",
    "                                                      colsample_bytree = 0.85, reg_alpha= 1e-9, reg_lambda= 3, \n",
    "                                                      objective='multi:softprob', \n",
    "                                                      num_class=8, early_stopping_rounds=20), \n",
    "                       param_grid = param_grid10, scoring= 'accuracy', n_jobs=4)\n",
    "\n",
    "grid_search10.fit(X_train_new, y_train)\n",
    "print(\"Grid search took {} minutes\".format((time()-start)/60))\n",
    "\n",
    "grid_search10.grid_scores_, grid_search10.best_params_, grid_search10.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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